Mejores libros de estadísticas

This book is a must-have for aspiring data scientists who lack formal statistical training. It explores statistical methods from a data science perspective and provides practical guidance on their application. The second edition includes comprehensive examples in Python and advice on avoiding their misuse. You'll learn all about exploratory data analysis, random sampling, experimental design, regression, and more. Plus, the book covers unsupervised learning methods for extracting meaning from unlabeled data. If you're familiar with R or Python and have some exposure to statistics, this accessible reference will bridge the gap for you.

Discover the world of data mining and machine learning with this comprehensive guide. Written by three prominent professors of statistics, this book provides a common conceptual framework for understanding the tools and ideas in various fields such as medicine, biology, finance, and marketing. With a focus on concepts rather than mathematics, it covers a broad range of topics including neural networks, support vector machines, classification trees, and boosting. With many examples and color graphics, this is a valuable resource for statisticians and anyone interested in data mining in science or industry.

Learn how to turn data into knowledge with an introduction to statistical analysis using Python. By working through a single case study, this book teaches the entire process of data analysis, from collecting data to identifying patterns and testing hypotheses. Discover distributions, rules of probability, visualization, and more through writing and testing code. With new chapters on regression, time series analysis, survival analysis, and analytic methods, gain a deeper understanding of statistical inference to answer real-world questions.

Learn probability and statistics quickly with this book, covering a broad range of topics beyond typical introductory math books. Perfect for graduate or advanced undergrads in computer science, math, statistics, and related disciplines. Get familiar with modern topics like nonparametric curve estimation, bootstrapping, and classification, without needing prior knowledge. Discover the intersection of statistics, data mining, and machine learning, and how statisticians and computer scientists are working together to advance these fields.

A straightforward and accessible guide to statistics, this textbook covers the basics and beyond, from central tendency to advanced concepts like regression and ANOVA. Each chapter includes a short description of the statistic, an in-depth explanation, an example, and a glossary of terms. The fourth edition features work problems and examples from the author's own data and published research, as well as a website with PowerPoint presentations, interactive problems, and more. Perfect for undergrad or graduate statistics courses, or as a reference tool for anyone interested in refreshing their memory.

Discover the fascinating world of statistics with an engaging and interactive book that brings this typically dry subject to life. With Head First Statistics, you'll learn everything you want and need to know about statistics through thought-provoking material, full of puzzles, stories, quizzes, and real-world examples. Whether you're a student or just curious, this brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Master topics such as probability, expectation, sampling, regression, hypothesis testing, and much more. Ideal for high school and college students, Head First Statistics satisfies the requirements for passing the AP Statistics Exam. Get ready to see how statistics work in the real world!

Learn how to avoid common statistical fallacies and analyze data correctly with "Statistics Done Wrong". This book offers cautionary tales of mistakes made by even the most knowledgeable scientists and teaches you the best practices for avoiding those errors. Discover the ideas behind hypothesis testing and regression analysis, how to ask the right questions, design experiments, and work with data. Through colorful examples of statistics gone awry, this book offers approachable lessons for proper methodology, and pro tips for practicing scientists and statisticians. Improve your data analysis skills with "Statistics Done Wrong".

La guía definitiva del pensamiento estadístico. Las estadísticas están en todas partes, tanto en la ciencia como en los negocios, y en los medios de comunicación populares cientos de veces al día. En esta era de los grandes datos, un conocimiento básico de la estadística es más importante que nunca si queremos separar los hechos de la ficción, los adornos ostentosos de las pruebas en bruto, y más aún si esperamos participar en el futuro, en lugar de ser simples espectadores. En ‘El arte de la estadística’, el reconocido estadístico David Spiegelhalter muestra a los oyentes cómo extraer conocimientos de los datos en bruto centrándose en los conceptos y las conexiones que hay detrás de las matemáticas. Basándose en ejemplos del mundo real para introducir cuestiones complejas, nos muestra cómo la estadística puede ayudarnos a determinar cuál fue el pasajero más afortunado del Titanic, si un famoso asesino en serie podría haber sido capturado antes y si el cribado del cáncer de ovarios es beneficioso. ‘El arte de la estadística’ no sólo nos muestra cómo los matemáticos han utilizado la ciencia estadística para resolver estos problemas, sino que también nos enseña cómo podemos pensar como estadísticos. Aprendemos a aclarar nuestras preguntas, suposiciones y expectativas al abordar un problema, y -quizás aún más importante- aprendemos a interpretar responsablemente las respuestas que recibimos.